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Field
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Applicants are invited for the posts of Postdoctoral Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University
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language requirement of the UK HEI; Have a background or a proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate
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, targeting dual-use applications for both military and civilian purposes. The fellow will mainly work on robot development and autonomous navigation but based on the interests of the PhD fellow there are also
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background/interest in time-series analysis, theoretical machine learning on networks, and high-dimensional statistics. Key Responsibilities: Take the lead in developing sub-projects (problem formulation
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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. To learn more about ongoing research in Saha laboratory please visit https://www.kumc.edu/school-of-medicine/academics/departments/radiation-oncology/research/saha-lab-of-radiation-biology.html. Job
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are expected to submit a career development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after commencement of the fellowship period. The
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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investigators to acquire essential academic credentials and research skills with the purpose of increasing competitiveness for MD, PhD, or MD-PhD programs in biomedical sciences. Primary Responsibilities Devote
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, output validation and reporting. Developing integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning